Deep Fake Detection: Survey of Facial Manipulation Detection Solutions

• URL: http://arxiv.org/abs/2106.12605v1
• Date: Wed, 23 Jun 2021 18:08:07 GMT
• Title: Deep Fake Detection: Survey of Facial Manipulation Detection Solutions
• Authors: Samay Pashine, Sagar Mandiya, Praveen Gupta, and Rashid Sheikh
• Abstract summary: We analyze several states of the art neural networks (MesoNet, ResNet-50, VGG-19, and Xception Net) and compare them against each other. We find an optimal solution for various scenarios like real-time deep fake detection to be deployed in online social media platforms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Learning as a field has been successfully used to solve a plethora of complex problems, the likes of which we could not have imagined a few decades back. But as many benefits as it brings, there are still ways in which it can be used to bring harm to our society. Deep fakes have been proven to be one such problem, and now more than ever, when any individual can create a fake image or video simply using an application on the smartphone, there need to be some countermeasures, with which we can detect if the image or video is a fake or real and dispose of the problem threatening the trustworthiness of online information. Although the Deep fakes created by neural networks, may seem to be as real as a real image or video, it still leaves behind spatial and temporal traces or signatures after moderation, these signatures while being invisible to a human eye can be detected with the help of a neural network trained to specialize in Deep fake detection. In this paper, we analyze several such states of the art neural networks (MesoNet, ResNet-50, VGG-19, and Xception Net) and compare them against each other, to find an optimal solution for various scenarios like real-time deep fake detection to be deployed in online social media platforms where the classification should be made as fast as possible or for a small news agency where the classification need not be in real-time but requires utmost accuracy.

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